Abstract
We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain. Then, we develop an agent, called Orator, to accomplish the OIN task, and assemble the Orator and the recently proposed OIE agent — Logician into a dual system to utilize the duality structure with a reinforcement learning paradigm. Experimental results reveal the dual structure between OIE and OIN tasks helps to build better both OIE agents and OIN agents.- Anthology ID:
- D18-1236
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2119–2130
- Language:
- URL:
- https://aclanthology.org/D18-1236
- DOI:
- 10.18653/v1/D18-1236
- Cite (ACL):
- Mingming Sun, Xu Li, and Ping Li. 2018. Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2119–2130, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (Sun et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D18-1236.pdf